Curated items for this scope are still being collected. Below are the latest related updates; items meeting the curation criteria will be marked separately.
New York University Professor Aswath Damodaran reviewed the performance of US stocks in September 2026: the 10-year US Treasury yield rose from 4.75% to 5.29% during the month, an increase of 54 basis points, placing it in the top 10% of 770 monthly changes since 1962. Despite this, the total market capitalization of US stocks still increased by $2.5 trillion during the month, with nearly $1.5 trillion of that increase coming from the technology sector.
NYU valuation professor Aswath Damodaran published a lengthy article proposing a four-stage framework for revolutionary change, arguing that AI is currently entering its coming-of-age phase, shifting from investment and infrastructure building to commercial implementation. The article notes that since the release of ChatGPT in November 2022, the Mag Seven contributed 45% of the total increase in U.S. stock market capitalization from 2022 to 2025. Six leading AI companies have cumulatively spent $1.7 trillion in capital expenditures, while the current upper bound of total revenue from AI products and services is only about $250 billion.
Aswath Damodaran, a valuation professor at New York University, uses the complete journey of Leopold Aschenbrenner's Situational Awareness hedge fund—from explosive growth to liquidation following a blowup—as a case study to systematically break down the definition, sources, and real-world impact of investment conviction.
NYU Professor Aswath Damodaran analyzes whether SpaceX, OpenAI, and Anthropic should be included in the S&P 500 after their IPOs. He breaks down index construction, including constituent selection, weighting methods, and index unit conversion mechanisms. He notes that the S&P 500 uses free-float market capitalization weighting, and that the June 15, 2026 closing level of 7554.29 corresponds to an index unit of 0.1190.
Drawing on two academic papers, the author identifies three structural limitations of using LLMs in trading research: First, training data is saturated with trading common sense and lacks mechanism-driven edge thinking. Second, across 35+ models, accuracy in time-series value tracking declines logarithmically as the number of historical updates increases, and neither prompt engineering nor expanding the context window can fix this. Third, outputs from 25+ models on open-ended questions show 71%–82% similarity, and models with different architectures converge to the same common answers.
Bridgewater Co-Chief Investment Officer Greg Jensen and AIA Labs Chief Scientist Jas Sekhon released a viewpoint arguing that Google Gemini 3 is currently the strongest publicly available general-purpose large AI model, with pre-training compute at least 2–3 times higher than GPT-4o, validating that the path of scaling pre-training remains sustainable. This development will drive the AI industry to continue increasing investment in compute, chips, and power, with significant implications for capital markets and the economy.
